JP2022023582A - Servo motor device and control method - Google Patents

Abstract

To reduce downtime caused when a servomotor device is replaced.SOLUTION: A servo motor device 1 includes a motor unit 3 and a speed reducer 4 for reducing and outputting rotation of the motor unit 3. A control device 2 includes: a detection unit 22 for acquiring detection information on operation of the motor unit 3; and an arithmetic unit 24 for generating an approximation curve based on transition in a time series of a life prediction parameter calculated by using the detection information to calculate life prediction information (a residual life time RT) based on the generated approximation curve.SELECTED DRAWING: Figure 1

Description

The present invention relates to the field of a servomotor device and a control method thereof, and particularly to the field of life prediction of the servomotor device.

In recent years, for example, a servomotor device has been used in a collaborative robot or the like.
Patent Document 1 discloses a technique for identifying a failed servomotor device based on operation information of a collaborative robot before detecting an abnormality in the operation of the collaborative robot.

Japanese Unexamined Patent Publication No. 2015-217468

Servo motor devices mounted on collaborative robots and the like reach the end of their product life and fail when used for a long period of time.
If the servo motor device fails, the user will arrange a new servo motor device for replacement or have the supplier of the collaborative robot replace the servo motor device. At this time, the new servo motor device will be installed. During the period from the arrangement to the completion of the exchange, there will be a time (downtime) during which the collaborative robot cannot work.
In order to reduce such downtime, it is necessary to predict the period until the servomotor device reaches the end of its life and prepare for replacement.

Therefore, an object of the present invention is to reduce the downtime that occurs when the servomotor device is replaced by predicting the life of the servomotor device.

The servomotor device according to the present invention is a servomotor device including a motor unit that generates a driving force and a speed reducer that decelerates and outputs the rotation of the motor unit, and detection information regarding the operation of the motor unit. A detection unit that acquires To prepare for.
This makes it possible to notify the user of the replacement timing of the servomotor device before the servomotor device reaches the end of its life. Further, the life of the servomotor device is predicted not on the main controller side of the robot device or the like on which the servomotor device is mounted, but on the servomotor device.
The parameters calculated here using the detection information are, for example, torque constant, moment of inertia, viscous resistance, static friction coefficient, gravity influence coefficient, backlash amount, energy efficiency, high frequency vibration coefficient, natural frequency, heat generation coefficient, heat dissipation coefficient, And the maximum static friction force of the speed reducer.
The life prediction information is information indicating the period from the time when the latest detection information is acquired to the time when the servomotor device is predicted to reach the end of its life (hereinafter, also referred to as the remaining life period).

The servomotor device according to the present invention described above includes a memory unit in which the detection information is recorded in a storage area provided for each predetermined numerical range, and the memory unit corresponds to the predetermined numerical range in the storage area. It is conceivable that the detection information is recorded in the recording area.
As a result, a plurality of detection information included in the predetermined numerical range are collectively recorded.

In the servomotor device according to the present invention described above, it is conceivable that the detection information that has been averaged with the value already recorded in the corresponding recording area is recorded in the memory unit. ..
As a result, the value of the recorded detection information approaches the value detected in the center of the predetermined numerical range of the storage area.

The control method according to the present invention is a control method for a servomotor device including a motor unit that generates a driving force and a speed reducer that decelerates and outputs the rotation of the motor unit, and relates to an operation of the motor unit. The process of acquiring the detection information, the process of generating an approximation curve based on the transition of the parameters calculated using the detection information in the time series, and the process of calculating its own life prediction information based on the generated approximation curve. Is executed by the servo motor device.

According to the present invention, it is possible to reduce the downtime that occurs when the servomotor device is replaced.

It is a figure which shows the structure of the servomotor device in embodiment of this invention. It is a conceptual diagram of the storage area of the memory part of this embodiment. It is a figure which shows the flowchart of the process to execute the control apparatus of this embodiment. It is a figure which shows the flowchart of the process to execute the control apparatus of this embodiment. It is a conceptual diagram about the calculation function of the life prediction information of this embodiment. It is a conceptual diagram of the storage area of the memory part of this embodiment.

Hereinafter, embodiments of the present invention will be described in the following order.
<1. Servo motor device configuration>
<2. Example of detection information recording processing>
<3. Example of calculation processing of life prediction information>
<4. Summary and modification>

The present embodiment will be described with reference to FIGS. 1 to 6. The drawings extract and show the configurations of the main parts and their surroundings, which are deemed necessary for the explanation. The drawings are schematic, and the dimensions, ratios, etc. of each structure described in the drawings are merely examples. Therefore, various changes can be made according to the design and the like as long as the technical idea of the present invention is not deviated.

<1. Servo motor device configuration>
FIG. 1 shows a configuration example of the servomotor device 1 of the present embodiment.
The servomotor device 1 is mounted on the collaborative robot as, for example, a joint unit for driving the joints of the collaborative robot that works in the same space as a human. The servomotor device 1 operates based on, for example, a control signal from the main controller of the collaborative robot or a program recorded in the servomotor device 1.

The servomotor device 1 can be mounted on various robots in addition to joint arm robots such as collaborative robots. The servomotor device 1 includes various robots such as a service robot, a home robot, etc., specifically, a mobile robot, a boarding robot, a transport robot, an operation unit that operates by combining one or a plurality of servomotor devices 1, an opening / closing device, and the like. Can be mounted on.

The servomotor device 1 includes a control device 2, a motor unit 3, a speed reducer 4, various sensors 5, and a memory unit 6.

The control device 2 is configured to include, for example, a microcomputer equipped with a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), etc., and the CPU executes processing according to a program. Controls the operation of the control device 2.
The control device 2 has functions as an operation control unit 21, a detection unit 22, a recording control unit 23, and a calculation unit 24.

In this embodiment, an example in which various functions of the control device 2 are configured as one CPU will be described. It should be noted that some or all of the various functions of the control device 2 may be configured as different CPUs. In this case, the present embodiment is realized by each CPU performing each process in parallel.

The operation control unit 21 in the control device 2 executes operation control of various devices constituting the servomotor device 1. The operation control unit 21 executes operation control of the motor unit 3 and the speed reducer 4 according to, for example, a predetermined target locus. Further, the operation control unit 21 executes operation control of the motor unit 3 and the speed reducer 4 based on the detection information from the various sensors 5.
The control signal by the operation control unit 21 is output to the motor unit 3 and the speed reducer 4 via an inverter of a control device 2 (not shown).

The motor unit 3 is driven in response to a control signal from the operation control unit 21. By driving the motor unit 3, the operation of the drive target Tg in the servomotor device 1 is controlled. The speed reducer 4 reduces the rotation of the motor unit 3. The motor unit 3 and the speed reducer 4 are geared motors in which the rotation axis and the output axis of the motor match.

The motor unit 3 is, for example, a brushless DC (Direct Current) motor. The motor unit 3 is not limited to the brushless DC motor, and various motors can be applied. For example, the motor unit 3 may be a DC motor with a brush.
The driving force generated by the motor unit 3 is transmitted to the drive target Tg in the collaborative robot via the speed reducer 4 that slows down the rotation of the motor unit 3.

The detection unit 22 in the control device 2 acquires detection information (hereinafter, also simply referred to as detection information) regarding the operation of the motor unit 3. The detection unit 22 acquires detection information from various sensors 5 provided in the servomotor device 1.

The various sensors 5 include, for example, an input voltage sensor 51, an input current sensor 52, an output voltage sensor 53, an output current sensor 54, a motor output shaft encoder 55, a speed reducer output shaft encoder 56, a temperature sensor 57, and an IMU (Inertial Measurement Unit). ) 58.

The input voltage sensor 51 detects the voltage input to the control device 2 from the power supply VDD. The input voltage sensor 51 is provided between the power supply VDD and the control device 2. For example, one input voltage sensor 51 is provided.

The input current sensor 52 detects the current input to the control device 2 from the power supply VDD. The input current sensor 52 is provided between the power supply VDD and the control device 2. For example, one input current sensor 52 is provided.

The output voltage sensor 53 detects the voltage output from the control device 2 to the motor unit 3 as detection information. Further, the output voltage sensor 53 detects the counter electromotive force as detection information by cutting off the drive of the inverter while the motor unit 3 is rotating.

The output voltage sensor 53 is provided between the control device 2 and the motor unit 3.
When the motor unit 3 is a brushless DC motor, three output voltage sensors 53 are provided. The number of output voltage sensors 53 varies depending on the type of the motor unit 3. For example, when the motor unit 3 is a DC motor with a brush, one output voltage sensor 53 may be provided.

The output current sensor 54 detects the current output from the control device 2 to the motor unit 3 as detection information.
The output current sensor 54 is provided between the control device 2 and the motor unit 3. When the motor unit 3 is a brushless DC motor, three output current sensors 54 are provided. The number of output current sensors 54 varies depending on the type of motor unit 3. For example, when the motor unit 3 is a DC motor with a brush, one output current sensor 54 may be provided.

The motor output shaft encoder 55 detects the rotation angle and the angular speed of the output shaft of the motor unit 3 (hereinafter, also referred to as a motor output shaft) as detection information. The motor output shaft encoder 55 is provided on the motor output shaft.

The speed reducer output shaft encoder 56 detects the rotation angle and the angular speed of the output shaft of the speed reducer 4 (hereinafter, also referred to as a speed reducer output shaft) as detection information. The speed reducer output shaft encoder 56 is provided on the speed reducer output shaft.

The temperature sensor 57 is provided in the motor unit 3 and detects the temperature of the motor unit 3 as detection information.

The IMU 58 detects the ground angle of the servomotor device 1 as detection information from the mounted acceleration sensor or gyro sensor. By mounting the IMU 58 on the servomotor device 1, it is possible to detect the ground angle of the servomotor device 1 regardless of the mounting angle of the servomotor device 1 to the collaborative robot.
Further, the IMU 58 detects the vibration of the servomotor device 1 generated from the rotation of the motor unit 3 as detection information.

The various sensors 5 acquire the above-mentioned detection information for each sensor, and supply the acquired detection information to the control device 2. That is, the detection information used for life prediction can be acquired by the various sensors 5 provided in the servomotor device 1 without attaching an external device or the like to the servomotor device 1.

The recording control unit 23 in the control device 2 causes the memory unit 6 to record the detection information acquired by the detection unit 22. The memory unit 6 is configured as, for example, a RAM.
The memory unit 6 may be a detachable recording medium such as a memory card, an optical disk, or a magnetic tape, or may be a fixed type HDD (Hard Disk Drive), a semiconductor memory module, or the like. Further, the memory unit 6 may be provided in the control device 2. For example, the RAM of the control device 2 may function as the memory unit 6.

The memory unit 6 is provided with a storage area for recording the detection information in a multidimensional array according to the type of the detection information. Detection information is recorded in the storage area for each predetermined numerical range.

The storage area of the memory unit 6 is formed, for example, in a two-dimensional array as shown in FIG. In FIG. 2, for example, the column is divided into about 333 rad / s ² as the angular acceleration of the motor shaft, and the row is divided into about 166 rad / s as the angular velocity of the motor shaft. Then, in each of the divided cells, the current value corresponding to the predetermined numerical range of the angular acceleration and the angular velocity of each cell is recorded.
By such a method, the detection information acquired from the various sensors 5 is recorded in the memory unit 6. The details of the detection information recording method will be described later.
Although an example of a two-dimensional array is shown in FIG. 2, the storage area of the memory unit 6 can be formed as a multidimensional array of three-dimensional array or more depending on the number of detection information.

The calculation unit 24 in the control device 2 acquires the detection information from the various sensors 5 from the memory unit 6. The calculation unit 24 calculates (identifies) the life prediction parameter used for the life prediction of the servomotor device 1 based on the acquired detection information.

The life prediction parameters are, for example, torque constant, moment of inertia, viscous resistance, static friction coefficient, gravity influence coefficient, backlash amount, energy efficiency, high frequency vibration coefficient, natural frequency, heat generation coefficient, heat dissipation coefficient, and maximum stationary of the speed reducer 4. It is a value indicated by a physical quantity such as a frictional force. Details of the calculation method of various life prediction parameters will be described later.

Further, the calculation unit 24 calculates the life prediction information of the servomotor device 1 based on the calculated life prediction parameters.
The calculation unit 24 calculates the life prediction information of the servomotor device 1 based on the above-mentioned life prediction parameter. The calculation unit 24 can predict the life of the servomotor device 1 by calculating at least one value among the various life prediction parameters described above.

The control device 2 transmits, for example, the life prediction information calculated via wireless communication or wired communication to the main controller of the collaborative robot.
Based on the life prediction information received from the control device 2, the May controller tells the user that the servomotor device 1 is about to reach the end of its life (recommended replacement timing) before the servomotor device 1 reaches the end of its product life. Have them present it so that they can be recognized.

Since there is a certain period from the recommended replacement timing to the life of the servomotor device 1, the user who confirms that the recommended replacement timing is ready for replacement, such as arranging the servomotor device 1 to be replaced. I do. As a result, it is possible to secure a period until the servomotor device 1 is replaced, so that a substitute servomotor device 1 can be prepared before the servomotor device 1 reaches the end of its life. This makes it possible to reduce the downtime that may occur when the servo motor device 1 is replaced.

<2. Example of detection information recording processing>
An example of recording processing of detection information executed by the control device 2 in order to realize the present embodiment will be described with reference to FIG.

First, in step S101, the control device 2 acquires detection information from the various sensors 5 described above. The correspondence between the various sensors 5 and the detected information to be acquired is as follows, for example.
Input voltage sensor 51 Voltage input to the control device 2 / Input current sensor 52 Current / output voltage sensor 53 input to the control device 2 Countercurrent power and voltage / output current sensor 54 output to the motor unit 3 Motor Current output to section 3 ・ Motor output shaft encoder 55 Rotation angle and angular speed of motor output shaft ・ Reducer output shaft encoder 56 Rotation angle and angular speed of reducer output shaft ・ Temperature sensor 57 Temperature of motor section 3 ・ IMU58 Servo motor Ground angle of device 1 and vibration of servo motor device 1

In the next step S102, the control device 2 sets a recording area for recording the detection information in the storage area of the memory unit 6. As shown in FIG. 2, the storage area has a plurality of cells separated by a predetermined numerical range. The control device 2 sets a cell in a predetermined numerical range including the detection information in the storage area as a recording area.

Subsequently, in step S103, the control device 2 performs an averaging process on the detection information recorded in the set recording area and the detection information to be recorded from now on.
Then, in step S104, the control device 2 records the detection information subjected to the averaging process in the recording area.

After the process of step S104, the control device 2 ends the process of FIG. The control device 2 repeatedly executes the process of FIG. 3 during the operation of the collaborative robot.
As a result, the detection information by the various sensors 5 is recorded in the memory unit 6.

<3. Example of calculation processing of life prediction information>
An example of the calculation processing of the life prediction information executed by the control device 2 in order to realize the present embodiment will be described with reference to FIG.

First, in step S201, the control device 2 acquires the detection information from the various sensors 5 from the memory unit 6. Then, in step S202, the control device 2 calculates (identifies) the life prediction parameter used for the life prediction of the servomotor device 1 based on the acquired detection information.

The life prediction parameters are, for example, torque constant, moment of inertia, viscous resistance, static friction coefficient, gravity influence coefficient, backlash amount, energy efficiency, high frequency vibration coefficient, natural frequency, heat generation coefficient, heat dissipation coefficient, and maximum stationary of the speed reducer 4. Such as frictional force.
Hereinafter, the identification method of each parameter will be described.

(1) Torque constant The control device 2 acquires the counter electromotive force detected by the output voltage sensor 53 and the angular velocity of the motor output shaft detected by the motor output shaft encoder 55 as detection information from the memory unit 6. The control device 2 defines the value ω ⁺ by the following [Equation 1] and identifies the torque constant.

The meaning of each symbol in the above [Equation 1] is as follows.
・ V _rms counter electromotive force ・ ω angular velocity of motor output shaft ・_{k v} counter electromotive force constant ・ kt torque constant

The control device 2 identifies the counter electromotive force constant by using a pseudo-reciprocal based on a plurality of different angular velocity and counter electromotive force measurement data sets. Since the counter electromotive force constant and the torque constant have the same value, the control device 2 identifies the torque constant by identifying the counter electromotive force constant.
As the value ω, the angular velocity of the reducer output shaft detected by the reducer output shaft encoder 56 can be used instead of the angular velocity of the motor output shaft. This also makes it possible to identify the torque constant.

(2) Identification of inertial moment, viscous resistance, static friction coefficient, and gravity influence coefficient The control device 2 uses the counter electromotive force detected by the output voltage sensor 53, the current detected by the output current sensor 54, and the motor output shaft encoder 55. The detected rotation angle and angular speed of the motor output shaft and the ground angle of the servo motor device 1 detected by the IMU 58 are acquired from the memory unit 6 as detection information.

Further, the control device 2 identifies the torque constant by the above [Equation 1].
The control device 2 identifies the moment of inertia, the viscous resistance, the coefficient of static friction, and the coefficient of influence of gravity based on [Equation 3] which is a modification of the following [Equation 2].

The meanings of the symbols in the above [Equation 2] and [Equation 3] are as follows.
・ Ω Angular velocity of motor output shaft ・_kt torque constant ・ sign (ω) Function that returns either 1, -1, or 0 according to the sign of the angular velocity value ・ θ Angle of motor output shaft to ground ・ J inertia Moment of inertia, B viscous resistance, C static friction coefficient, D, E gravity influence coefficient, I current flowing through the motor unit 3.

Here, the ground angle of the motor output shaft is calculated by adding the rotation angle of the motor output shaft and the ground angle of the servomotor device 1.
Further, (d / dt) ω in the above [Equation 2] and [Equation 3] is calculated by the time derivative of the angular velocity of the motor output shaft detected by the motor output shaft encoder 55. If the measured angular velocity is differentiated without removing the noise component, the value of (d / dt) ω is far from the true value. Therefore, the control device 2 differentiates the measured angular velocity data while performing noise processing by, for example, the SG (Savitzky Golay) method.
As the value ω, the angular velocity of the reducer output shaft detected by the reducer output shaft encoder 56 can be used instead of the angular velocity of the motor output shaft. This also makes it possible to identify the torque constant. At this time, the value θ is calculated by adding the rotation angle of the reducer output shaft and the ground angle of the servomotor device 1.

Since the values J, B, C, D, and E are parameters peculiar to the motor unit 3 and are unknown, they are identified by the following [Equation 4] from a plurality of measurement data.

Since the value _kt of the torque constant is known from the above [Equation 1], the control device 2 defines the values W, X, W ⁺ by the above [Equation 4], and the values J, B, C, D, E. To identify.

(3) The backlash amount control device 2 uses the memory unit 6 as detection information of the rotation angle of the motor output shaft detected by the motor output shaft encoder 55 and the rotation angle of the reducer output shaft detected by the reducer output shaft encoder 56. Get from.
The control device 2 identifies the amount of backlash by the following [Equation 5].

The meaning of each symbol in the above [Equation 5] is as follows.
・ Θ _L reduction gear output shaft rotation angle ・ θ _M motor output shaft rotation angle ・ j _θ backlash amount ・ n reduction ratio The rotation angle is measured at multiple angles and determined to a unique value by the least squares method.

(4) Energy efficiency The energy efficiency here is the inverter efficiency of the control device 2 and the motor efficiency of the motor unit 3.
First, the method for identifying the inverter efficiency will be described.
In identifying the inverter efficiency, the control device 2 has an input voltage to the control device 2 detected by the input voltage sensor 51, an input current to the control device 2 detected by the input current sensor 52, and an output voltage sensor 53. The output voltage to the motor unit 3 detected by the above and the output current to the motor unit 3 detected by the output current sensor 54 are acquired from the memory unit 6 as detection information.

The control device 2 calculates the inverter input energy from the input voltage and the input current to the control device 2. Further, the control device 2 calculates the inverter output energy from the output voltage and the input current to the motor unit 3.
Then, the control device 2 defines the value W ⁺ _in by the following [Equation 6] and identifies the inverter efficiency. Since the inverter efficiency changes depending on the output value, the control device 2 measures the input energy when the output energy is an arbitrary value a plurality of times.

The meaning of each symbol in the above [Equation 6] is as follows.
・ W _in inverter input energy ・ W _{out_inv} inverter output energy ・ η _inv inverter efficiency

Next, the method for identifying the motor efficiency will be described.
First, the control device 2 calculates the motor output energy by the following [Equation 7].

The meaning of each symbol in the above [Equation 7] is as follows.
-W _out motor output energy-K _t torque constant-I current flowing through the motor unit 3-ω Angular velocity of the motor output shaft Torque constant K _t is calculated by the same method as in [Equation 1] above.

Further, since the motor input energy has the same value as the inverter output energy, the control device 2 defines the value W ⁺ _{in_mot} by the following [Equation 8] and [Equation 9], and calculates the motor input energy.

The meaning of each symbol in the above [Equation 8] is as follows.
-W _out motor output energy-W _{in_mot} motor input energy The motor output energy value W _out is calculated by the above [Equation 7].

The control device 2 identifies the motor efficiency by the following [Equation 9] using the motor output energy and the motor input energy measured a plurality of times.

The meaning of each symbol in the above [Equation 9] is as follows.
-W _out Motor output energy-W _{in_mot} Motor input energy-η _mot Inverter efficiency Motor output energy value W _out is calculated from the above [Equation 7]. Further, the value W _{in_mot} of the motor input energy is calculated from the above [Equation 8].

(5) High-frequency vibration coefficient and natural frequency The control device 2 acquires the vibration information of the servomotor device 1 detected from the IMU 58 as detection information from the memory unit 6 in identifying the high-frequency vibration coefficient and the natural frequency.

The control device 2 performs FFT (Fast Fourier Transform) analysis on the acquired vibration information, and from the high-wavelength amplitude of the vibration frequency with respect to the rotation speed, the vibration coefficient (high-wavelength vibration coefficient) for each high wavelength. To identify.

Further, the control device 2 estimates the natural frequency of the servomotor device 1 including the load by measuring the vibration while changing the rotation speed of the motor unit 3.

(6) Heat generation coefficient and heat dissipation coefficient The control device 2 acquires the temperature of the motor unit 3 detected from the temperature sensor 57 as detection information from the memory unit 6 when identifying the heat generation coefficient and heat dissipation coefficient of the motor unit 3.

The heat generation factors of the motor unit 3 are mainly copper loss and iron loss of the motor, and heat generation due to friction in the speed reducer 4. Therefore, the factors that determine the calorific value are the current and the rotation speed of the motor. Further, the hysteresis loss and the eddy current loss of the iron loss of the motor change depending on the frequency of the current flowing through the coil, and the frequency flowing through the coil is proportional to the rotation speed of the motor in the brushless DC motor (motor unit 3). Therefore, the control device 2 identifies the heat generation coefficient and the heat dissipation coefficient of the motor unit 3 by the following [Equation 10].

The meaning of each symbol in the above [Equation 10] is as follows.
・ T _n Current temperature of motor unit 3 ・ T _n-1 Temperature of motor unit 3 detected last time ・ T ₀ Atmospheric temperature ・ R _a Motor resistance value ・ K _e vortex current loss coefficient ・ K _a velocity proportional loss coefficient K _b Heat dissipation coefficient of the motor unit 3 The atmospheric temperature here is the temperature of the motor unit 3 after a sufficient time has elapsed while the motor is stopped. The value Ka of the velocity proportional loss coefficient is _a value obtained by adding the loss due to friction and the hysteresis loss.
When heat generation due to friction is dominant, it can be approximated only by the terms K _e and K _b in the above [Equation 10]. Similarly, when the rotation speed of the used area is low and heat generation due to the current is dominant, it can be approximated only by the terms of the value R _a and the value K _b .

(7) The maximum static friction force control device 2 of the speed reducer 4 acquires detection information from the output current sensor 54, the speed reducer output shaft encoder 56, the temperature sensor 57, the IMU 58, and the like. The control device 2 may acquire the detection information from the motor output shaft encoder 55 instead of the reducer output shaft encoder 56.

The control device 2 gently raises the current (motor current) output to the motor unit 3 from the value of "0" so that the influence of inertia can be ignored. Then, the control device 2 increases the motor current until the reducer output shaft encoder 56 starts to rotate. At this time, the control device 2 acquires the motor current value at the start of rotation of the speed reducer output shaft encoder 56 from the output current sensor 54 and records it in the memory unit 6.
The control device 2 executes the above series of processes at the positions of the plurality of reducer output shafts in both the forward and reverse directions of the output shafts, acquires the motor current value at each angle θ of the output shafts, and obtains the motor current value. Record in the memory unit 6.

The control device 2 identifies the maximum static friction force of the speed reducer 4 by the following [Equation 11]. At this time, the control device 2 uses the angle θ, the current value, and the torque constant recorded in the memory unit 6 in identifying the maximum static friction force of the speed reducer 4. The torque constant is identified, for example, from the above [Equation 1].

The meaning of each symbol in the above [Equation 11] is as follows.
・ Τ _smax maximum static friction force ・ τ _mot Motor torque at the start of rotation ・ τ _g Vertical downward force generated by eccentric load

By the above method, the control device 2 identifies various life prediction parameters in step S202. The control device 2 may identify all of the various life prediction parameters, or may identify only any part of the parameters. Which of the life prediction parameters should be identified can be arbitrarily set in advance.

Subsequently, in step S203, the control device 2 generates an approximate curve based on the transition of the calculated (identified) life prediction parameter in the time series. Then, in step S204, the control device 2 calculates its own life prediction information based on the generated approximate curve.
The control device 2 generates an approximate curve for each identified life prediction parameter and calculates life prediction information.

FIG. 5 is a conceptual diagram of the function of calculating the life prediction information.
In the graph shown in FIG. 5, the time axis traveling to the right is shown as the horizontal axis, and the value of the life prediction parameter is shown as the vertical axis.

First, the control device 2 maps the life prediction parameters identified in step S202 for each time series (data D1, D2, D3, ... Dn). Here, the data Dn is the identified latest (latest) life prediction parameter.

The control device 2 generates an approximate curve that estimates the transition of the future life prediction parameter from the transition of the mapped life prediction parameter (parameter from the data D1 to the latest data Dn).

The control device 2 calculates the remaining life period RT from the time when the latest data Dn is mapped to the time when the generated approximate curve and the life threshold th indicating the life of the servomotor device 1 intersect, as the life prediction information.

Each mapped life prediction parameter contains errors that occur during identification. Therefore, the control device 2 generates approximate curves AC1 and AC2 in consideration of the error.
At this time, the range from the time when the approximate curve AC1 intersects the life threshold th to the time when the approximate curve AC2 intersects the life threshold th is defined as the error range ER. In this case, the accuracy of the life period RT based on the approximate curve AC3, which is the median value of the approximate curve AC1 and the approximate curve AC2, is the highest. However, considering the error that occurs when identifying the life prediction parameter, it is desirable to calculate the life prediction information including the error range ER.
The order of the approximate curve is arbitrarily designed to approximate the transition of the identified parameters for life prediction.

From the above, by calculating the life prediction information based on the transition of the approximate curve, it is possible to reduce the amount of data that needs to be acquired at the development stage as compared with the life prediction method using machine learning.

After the process of step S204, the control device 2 ends the process of FIG. The control device 2 repeatedly executes the process of FIG. 3 during the operation of the collaborative robot.
The control device 2 may execute the process of FIG. 4 during the work of the collaborative robot, or may perform the process during a preparatory exercise period such as a warm-up operation when the main power of the collaborative robot is turned on. May be executed.

Further, the control device 2 can execute the detection information recording process (FIG. 3) and the life prediction parameter calculation process (FIG. 4) at different timings. For example, the control device 2 executes the recording process of the detection information during the work of the collaborative robot, and executes the calculation process of the life prediction parameter after the work of the collaborative robot is completed or during the preparatory exercise period such as the warm-up operation. be able to. Further, the control device 2 may continuously execute the recording process of the detection information and the calculation process of the life prediction parameter, for example, during the operation of the collaborative robot.
From the above, the life prediction time (remaining life period RT) of the servomotor device 1 is calculated.

<4. Summary and modification>
In the servomotor device 1 including the motor unit 3 that generates the driving force of the above embodiment and the speed reducer 4 that decelerates and outputs the rotation of the motor unit 3, the control device 2 operates the motor unit 3. An approximate curve is generated based on the transition in the time series of the life prediction parameter calculated by using the detection information and the detection unit 22 that acquires the detection information, and the own life prediction information is generated based on the generated approximate curve. A calculation unit 24 for calculating (remaining life period RT) is provided (see FIGS. 4, 5, etc.).
This makes it possible to notify the user of the replacement timing of the servomotor device 1 before the servomotor device 1 reaches the end of its life.
Therefore, the user can design the repair plan of the servomotor device 1 in consideration of the replacement timing, and can arrange the replacement servomotor device 1 in advance before the end of its life. As a result, the downtime that occurs when the servo motor device 1 is replaced can be shortened.
In addition, by calculating the life prediction information based on the transition of the approximate curve, it is possible to reduce the amount of data that needs to be acquired at the development stage as compared with the life prediction using machine learning.

Further, the life of the robot is predicted not on the main controller side of the collaborative robot on which the servomotor device 1 is mounted, but on the servomotor device 1 side.
This makes it possible to reduce the processing load on the main controller side of the collaborative robot. This is particularly effective when a plurality of servomotor devices 1 are attached, for example, as a joint unit of a collaborative robot.

In the servo motor device 1 of the present embodiment, the control device 2 has, for example, a torque constant, a moment of inertia, a viscous resistance, a static friction coefficient, a gravity influence coefficient, a backlash amount, an energy efficiency, a high frequency vibration coefficient, a natural frequency, and a heat generation coefficient. One of the values of the heat dissipation coefficient and the maximum static friction force of the speed reducer 4 is calculated (identified) as a life prediction parameter (see S202 and the like in FIG. 4).

For example, by using the torque constant, moment of inertia, viscous resistance, static friction coefficient, and gravity influence coefficient used when executing the operation control of the motor unit 3 and the speed reducer 4 as parameters for life prediction, the operation control process can be performed. It is possible to improve the processing efficiency of the life prediction information calculation process.
Further, by identifying the inverter efficiency as the energy efficiency, the degree of deterioration of the inverter of the control device 2 can be estimated.

The servomotor device 1 of the present embodiment includes a memory unit 6 in which detection information is recorded in a storage area provided for each predetermined numerical range, and the memory unit 6 corresponds to a predetermined numerical range in the storage area. The detection information is recorded in the recording area (see S102 and the like in FIGS. 2 and 3).
As a result, a plurality of detection information included in the predetermined numerical range are collectively recorded.
Therefore, the amount of data recorded in the memory unit 6 can be reduced. In addition, by recording the detection information in the storage area as described above, it is possible to quickly determine whether the measured detection information is already recorded data, and the maximum number of data for various detection information has been determined. It can be recorded in the state.

In this embodiment, in the example of the storage area shown in FIG. 2, the column is the angular acceleration of the motor shaft at about 333 rad / s ² , and the row is the angular velocity of the motor shaft at about 166 rad / s. Although it is assumed that the storage area is divided at equal intervals, the storage area division method is not always evenly limited.
For example, in the vicinity of the center of the array of the storage region, the angular acceleration of the motor shaft may be subdivided from about 333 rad / s ² and divided into about 222 rad / s ² . In this way, the division interval of the storage area can be flexibly set according to the characteristics of the detection information to be measured.

In the servomotor device 1 of the present embodiment, the memory unit 6 records the detection information that has been averaged with the value already recorded in the recording area corresponding to the predetermined numerical range in the storage area. (See S103 and the like in FIGS. 2 and 3).
As a result, the value of the recorded detection information approaches the value detected in the center of the division interval of the storage area.
Therefore, it is possible to improve the quality of the detection information acquired from the memory unit 6.

Further, the memory unit 6 in the servomotor device 1 may be a multidimensional array in which the number of data of the corresponding detection information is recorded in the recording area corresponding to the predetermined numerical range in the storage area.

The storage area of the memory unit 6 is formed by, for example, a two-dimensional array as shown in FIG. In FIG. 6, for example, the column is divided into about 333 rad / s ² as the angular acceleration of the motor shaft, and the row is divided into about 166 rad / s as the angular velocity of the motor shaft. Then, in each of the divided cells (recording area), the number of data of the detection information corresponding to the predetermined numerical range of the angular acceleration and the angular velocity of each cell is recorded.

For example, in FIG. 6, the number of detection information data corresponding to the angular acceleration of the motor shaft of −1000 to −667 rad / s ² and the angular velocity of the motor shaft of −500 to 334 rad / s is recorded twice. Here, in the recording area where the angular acceleration of the motor shaft is 1 to 333 rad / s ² and the angular velocity of the motor shaft is 1 to 166 rad / s, the number of corresponding data is 18 times, which is the mode.

By acquiring the corresponding number of data in each recording area recorded in the memory unit 6, the control device 2 can calculate the coordinates of the mode, the standard deviation, the correlation coefficient, the covariance, and the like. It is possible to observe the transition of the mode coordinates, standard deviation, correlation coefficient, covariance, etc. at the same operation calculated in this way or when data is acquired a sufficient number of times. Based on the result, the life can be predicted in the same way as other life prediction parameters.

In the memory unit 6, for example, when analyzing a data group stored in a three-dimensional array in a two-dimensional plane, the control device 2 may analyze in parallel with each plane of the three-dimensional array. On the other hand, it is also possible to analyze on a plane defined diagonally.

The control method of the servomotor device 1 in the present embodiment is a control method of the servomotor device 1 including a motor unit 3 for generating a driving force and a deceleration device 4 for decelerating and outputting the rotation of the motor unit 3. Therefore, an approximation curve is generated based on the process of acquiring the detection information regarding the operation of the motor unit 3 and the transition of the life prediction parameter calculated by using the detection information in the time series, and based on the generated approximation curve. The servomotor device 1 executes a process of calculating its own life prediction information (remaining life period RT).
The control method as the embodiment can also obtain the same operation and effect as the servomotor device 1 as the above-described embodiment.

Finally, the effects described in the present disclosure are exemplary and not limited, and may have other effects or are part of the effects described in the present disclosure. You may.
Further, the embodiments described in the present disclosure are merely examples, and the technical scope of the present invention is not limited to the above-described embodiments. Therefore, it goes without saying that various changes can be made according to the design and the like as long as the technical idea of the present invention is not deviated from the above-described embodiment. It should be noted that not all combinations of configurations described in the embodiments are essential for solving the problem.

1 Servo motor device 2 Control device 3 Motor section 4 Reducer 5 Various sensors 6 Memory section 21 Operation control section 22 Detection section 23 Recording control section 24 Calculation section 51 Input voltage sensor 52 Input current sensor 53 Output voltage sensor 54 Output current sensor 55 Motor output shaft encoder 56 Reducer output shaft encoder 57 Temperature sensor

Claims (5)

A servomotor device including a motor unit that generates a driving force and a speed reducer that decelerates and outputs the rotation of the motor unit.
A detection unit that acquires detection information regarding the operation of the motor unit, and a detection unit.
A servomotor device including a calculation unit that generates an approximate curve based on the transition of parameters calculated using the detection information in a time series and calculates its own life prediction information based on the generated approximate curve. A memory unit for recording the detection information is provided in a storage area provided for each predetermined numerical range.
The servomotor device according to claim 1, wherein the detection information is recorded in the recording area corresponding to the predetermined numerical range in the storage area in the memory unit. The servomotor device according to claim 2, wherein the detection information that has been averaged with the value already recorded in the corresponding recording area is recorded in the memory unit. The calculation unit has torque constants, inertial moments, viscous resistance, static friction coefficient, gravity influence coefficient, backlash amount, energy efficiency, high frequency vibration coefficient, natural frequency, heat generation coefficient, heat dissipation coefficient, and maximum of the speed reducer as the parameters. The servo motor device according to any one of claims 1 to 3, which calculates any value of the static friction force. It is a control method of a servomotor device including a motor unit that generates a driving force and a speed reducer that decelerates and outputs the rotation of the motor unit.
The process of acquiring detection information regarding the operation of the motor unit and
Control that the servomotor device executes a process of generating an approximate curve based on the change in the time series of the parameter calculated using the detection information and calculating its own life prediction information based on the generated approximate curve. Method.

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